Data Mining (2nd Edition)

Name: Data Mining (2nd Edition)
ISBN: 9787121455025

王朝霞Publishing House of Electronics Industry · ChinaJun 2023

3.6

Word count: 210K words
Chapters: 79
ISBN: 9787121455025

Report an error Find this book

Summary

This book provides a systematic introduction to data mining theory, methods, and applications, covering data characterization and preprocessing, classical data mining algorithms (classification, regression, clustering, association rules, ensemble learning, etc.), and data analysis methods emerging under the new normal of big data (recommender systems, link analysis and web page ranking, Internet information extraction, log mining and query analysis, etc.). On this basis, each chapter includes a Python-based practical example. The book can serve as a textbook for courses in data science and big data technology at colleges and universities, as well as a textbook or reference book for programs in computer science, software engineering, and related fields. Related programs at vocational and technical colleges may also select portions of the content for teaching.

Editor's review

This book presents classical data mining content and new methods for the big data environment within a single framework. It begins with data characterization and preprocessing, then covers classical algorithms such as classification, regression, clustering, association rules, and ensemble learning, and extends to application areas including recommender systems, link analysis and web page ranking, Internet information extraction, and log mining and query analysis, forming a complete knowledge chain. A notable feature is that every chapter includes a Python-based practical example, allowing readers to verify what they have learned immediately after understanding the principles, rather than remaining at the level of formulas and concepts. As a second-edition textbook, its organization supports both classroom teaching and self-study: the theoretical sections help build a systematic framework, while the examples help develop operational skills. Readers are advised to proceed chapter by chapter, mastering preprocessing and classical algorithms first before moving on to the big data application chapters; working through the code alongside a Python environment yields better results. It is suitable as a course companion text and as a desk reference for technical professionals with some programming background who wish to systematically fill gaps in their data mining knowledge.

Who it's for

A good fit for

Students in data science and big data technology programs who need a course textbook and accompanying exercises
Computer science and software engineering students who want to systematically study data mining theory and Python implementations
Teachers and students in related programs at vocational and technical colleges, who may select certain chapters for teaching
Technical professionals with some programming background who wish to move into data analysis

Not a good fit for

Readers with no programming background at all who want a zero-barrier introduction to data mining
Readers who only want to quickly learn how to operate a particular tool and are unwilling to engage with algorithm principles
Readers looking for a monograph on advanced deep learning topics; this book does not focus on that area

Reading tips

Read in chapter order: complete the data preprocessing and classical algorithms sections first, then move on to the big data application chapters
After finishing each chapter, reproduce the example in a Python environment and compare the output to understand algorithm parameters and the meaning of results
Organize algorithms such as classification, clustering, and association rules into comparison tables, recording applicable scenarios, advantages, and disadvantages
When using the book as a textbook, work through small examples by hand alongside the end-of-chapter exercises first, then verify the conclusions with code

About the publisher

Publishing House of Electronics Industry China · Founded in 1982

The Publishing House of Electronics Industry is a comprehensive publisher distinguished by its focus on electronic information and related fields. Judging from the more than 3,000 titles in its catalogue, its publishing covers programming and design, artificial intelligence, computer science, finance and economics, photography, and social sciences, attending both to cutting-edge technology and to general readers. Representative titles include Factor Investing: Methods and Practice, x86 Assembly Language: From Real Mode to Protected Mode, The RISC-V Reader, Artificial Intelligence: Speech Recognition, Understanding and Practice, Phased Array Radar Technology, Radar Target Characteristics, and 100 Go Mistakes and How to Avoid Them, most of which centre on technical methods and engineering practice. Its authors include Shi Chuan, Li Zhong, Wang Xiaobo, David Patterson, Andrew Waterman, and Yu Zihao, among others from China and abroad. Owing to limited public information, no further publishing features are elaborated here.

版权信息
内容简介
编委会
总序
第2版前言
第1版前言
第1章绪论
1.1 数据挖掘的基本概念
1.2 数据挖掘的起源及发展
1.3 数据挖掘的常用工具
1.4 数据挖掘的应用
习题
参考文献
第2章数据特征分析及预处理
2.1 数据类型
2.2 数据特征分析
2.3 数据预处理
2.4 数据的相似性
习题
参考文献
第3章分类
3.1 分类概述
3.2 决策树
3.3 贝叶斯分类
3.4 支持向量机
3.5 实战：Python支持向量机分类
习题
参考文献
第4章回归
4.1 回归的基本概念
4.2 一元回归分析
4.3 多元线性回归分析
4.4 逻辑回归分析
4.5 其他回归分析
4.6 实战：获得最大有效率时的药物用量
习题
参考文献
第5章聚类
5.1 聚类基本概念
5.2 划分聚类方法
5.3 层次聚类方法
5.4 密度聚类方法
5.5 实战：Python聚类分析
习题
参考文献
第6章关联规则
6.1 基本概念
6.2 Apriori算法：通过限制候选产生发现频繁项集
6.3 FP-growth算法
6.4 其他关联规则算法
6.5 实战：购物篮关联规则挖掘
习题
参考文献
第7章集成学习
7.1 集成学习的概念
7.2 Bagging算法与随机森林算法
7.3 Boosting算法
7.4 结合策略
7.5 多样性
7.6 实战案例
7.7 本章小结
习题
参考文献
第8章推荐系统
8.1 推荐系统概述
8.2 基于内容的推荐
8.3 协同过滤推荐
8.4 基于标签的推荐
8.5 实战：搭建一个电影推荐系统
习题
参考文献
第9章互联网数据挖掘
9.1 链接分析与网页排序
9.2 互联网信息抽取
9.3 日志挖掘与查询分析
9.4 Python实战案例
习题
参考文献
附录A 大数据和人工智能实验环境

FAQ

Can I read this book without a machine learning background?

Yes, but you should first brush up on some basic probability, statistics, and linear algebra. The book starts with data characterization and preprocessing, and the classical algorithms section unfolds step by step; following along with the Python examples in each chapter makes the material easier to understand. Do not skip the foundational chapters; when you encounter formulas, first grasp their intuitive meaning, then return to the derivations.

What environment do the Python examples in the book require?

The materials state that each chapter includes a Python-based practical example, but no specific libraries or versions are specified. Readers can set up a common Python data analysis environment and run the code in the book step by step; if errors occur due to different library versions, consult the official documentation of the relevant library to adjust rather than skipping the example.

Is this book suitable for graduate entrance exam review or self-study examinations?

The book is positioned as a textbook or reference for relevant courses at colleges and universities, and its knowledge system is fairly complete, so it can be used as a review framework. However, when reviewing, the syllabus and designated textbooks of the target institution should take precedence; use this book as supplementary material for understanding and practice, focusing on the core chapters covering classification, clustering, association rules, and other topics in line with the syllabus.

How should time be allocated between the examples and the theoretical sections in each chapter?

It is advisable to spend roughly equal time on reading the theory and doing hands-on practice. First read through a chapter's theory to clarify the problem the algorithm solves and its basic steps, then run the example and try modifying parameters to observe how the results change. Reading without practice tends to leave you at the conceptual level, while practicing without reading makes it difficult to transfer skills to new problems.

Comments

Find this book

This site is an index: you start “Search all sources” in your browser, and the entries it lists all point to third-party sites; please judge their availability yourself.

About the author

王朝霞 Author

Public information about Wang Zhaoxia is limited, and what is available comes mainly from the six works published under her name. These works point in two directions. One is computing and data, including Data Mining and its second edition, which focus on methods and techniques and serve related courses and examinations. The other is music education and aesthetic education, covering an introduction to school music education and its teaching materials and methods, the alignment between college music teacher education and basic music education, and the professional quality of music teachers. Among these, the themes of rural music volunteer teaching and aesthetic education immersion reflect her sustained attention to combining college-level music teacher education with rural practice. Overall, her works combine the rigor of textbooks with a practical orientation and are suitable for teaching and reference reading.